Load raw data, annotate probes using biomaRt and load SFARI genes
# Load csvs
datExpr = read.csv('./../raw_data/RNAseq_ASD_datExpr.csv', row.names=1)
datMeta = read.csv('./../raw_data/RNAseq_ASD_datMeta.csv')
SFARI_genes = read_csv('./../working_data/SFARI_genes_01-15-2019.csv')
# Make sure datExpr and datMeta columns/rows match
rownames(datMeta) = paste0('X', datMeta$Dissected_Sample_ID)
if(!all(colnames(datExpr) == rownames(datMeta))){
print('Columns in datExpr don\'t match the rowd in datMeta!')
}
# Annotate probes
getinfo = c('ensembl_gene_id','external_gene_id','chromosome_name','start_position',
'end_position','strand','band','gene_biotype','percentage_gc_content')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL',
dataset='hsapiens_gene_ensembl',
host='feb2014.archive.ensembl.org') ## Gencode v19
datProbes = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=rownames(datExpr), mart=mart)
datProbes = datProbes[match(rownames(datExpr), datProbes$ensembl_gene_id),]
datProbes$length = datProbes$end_position-datProbes$start_position
# Group brain regions by lobes
datMeta$Brain_Region = as.factor(datMeta$Region)
datMeta$Brain_lobe = 'Occipital'
datMeta$Brain_lobe[datMeta$Brain_Region %in% c('BA4_6', 'BA9', 'BA24', 'BA44_45')] = 'Frontal'
datMeta$Brain_lobe[datMeta$Brain_Region %in% c('BA3_1_2_5', 'BA7')] = 'Parietal'
datMeta$Brain_lobe[datMeta$Brain_Region %in% c('BA38', 'BA39_40', 'BA20_37', 'BA41_42_22')] = 'Temporal'
datMeta$Brain_lobe=factor(datMeta$Brain_lobe, levels=c('Frontal', 'Temporal', 'Parietal', 'Occipital'))
#################################################################################
# FILTERS:
# 1 Filter probes with start or end position missing (filter 5)
# These can be filtered without probe info, they have weird IDS that don't start with ENS
to_keep = !is.na(datProbes$length)
datProbes = datProbes[to_keep,]
datExpr = datExpr[to_keep,]
rownames(datProbes) = datProbes$ensembl_gene_id
# 2. Filter samples from ID AN03345 (filter 2)
to_keep = (datMeta$Subject_ID != 'AN03345')
datMeta = datMeta[to_keep,]
datExpr = datExpr[,to_keep]
# 3. Filter samples with rowSums <= 40
to_keep = rowSums(datExpr)>40
datExpr = datExpr[to_keep,]
datProbes = datProbes[to_keep,]
#################################################################################
# Annotate SFARI genes
# Get ensemble IDS for SFARI genes
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL',
dataset='hsapiens_gene_ensembl',
host='feb2014.archive.ensembl.org') ## Gencode v19
gene_names = getBM(attributes=c('ensembl_gene_id', 'hgnc_symbol'), filters=c('hgnc_symbol'),
values=SFARI_genes$`gene-symbol`, mart=mart) %>%
mutate('gene-symbol'=hgnc_symbol, 'ID'=as.character(ensembl_gene_id)) %>%
dplyr::select('ID', 'gene-symbol')
SFARI_genes = left_join(SFARI_genes, gene_names, by='gene-symbol')
#################################################################################
# Add functional annotation to genes from GO
GO_annotations = read.csv('./../working_data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>%
mutate('ID' = as.character(ensembl_gene_id)) %>%
dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
mutate('Neuronal' = 1)
datExpr_backup = datExpr
rm(getinfo, to_keep, gene_names, mart)
Number of genes:
nrow(datExpr)
## [1] 33478
Gene count by SFARI score:
table(SFARI_genes$`gene-score`)
##
## 1 2 3 4 5 6
## 29 82 209 538 191 25
Gene count by brain lobe:
table(datMeta$Brain_lobe)
##
## Frontal Temporal Parietal Occipital
## 21 20 22 23
Gene count by SFARI score and brain lobe:
t(table(datMeta$Brain_lobe, datMeta$Diagnosis_))
##
## Frontal Temporal Parietal Occipital
## ASD 8 14 14 15
## CTL 13 6 8 8
Regions: Frontal, Temporal, Parietal and Occipital
y axis cut at 1000 to remove outliers
The distributions by score seem very similar between regions
make_ASD_vs_CTL_df = function(datExpr, lobe){
datMeta_lobe = datMeta %>% filter(Brain_lobe==lobe & rownames(datMeta) %in% colnames(datExpr))
datExpr_ASD = datExpr %>% data.frame %>% dplyr::select(which(datMeta_lobe$Diagnosis_=='ASD'))
datExpr_CTL = datExpr %>% data.frame %>% dplyr::select(which(datMeta_lobe$Diagnosis_!='ASD'))
ASD_vs_CTL = data.frame('ID'=as.character(rownames(datExpr)),
'mean_ASD'=rowMeans(datExpr_ASD), 'mean_CTL'=rowMeans(datExpr_CTL),
'sd_ASD'=apply(datExpr_ASD,1,sd), 'sd_CTL'=apply(datExpr_CTL,1,sd)) %>%
mutate('mean_diff'=mean_ASD-mean_CTL, 'sd_diff'=sd_ASD-sd_CTL) %>%
left_join(SFARI_genes, by='ID') %>%
dplyr::select(ID, mean_ASD, mean_CTL, mean_diff, sd_ASD, sd_CTL, sd_diff, `gene-score`) %>%
mutate('gene-score'=ifelse(is.na(`gene-score`),'None',`gene-score`)) %>%
mutate('gene-score'=ifelse(`gene-score`=='None' & ID %in% GO_neuronal$ID, 'Neuronal', `gene-score`))
return(ASD_vs_CTL)
}
p = list()
for(lobe in names(table(datMeta$Brain_lobe))){
datExpr_lobe = datExpr %>% dplyr::select(which(datMeta$Brain_lobe==lobe))
ASD_vs_CTL = make_ASD_vs_CTL_df(datExpr_lobe, lobe)
plot = ggplotly(ggplot(ASD_vs_CTL, aes(`gene-score`, abs(mean_diff), fill=`gene-score`)) +
geom_boxplot() + theme_minimal() + ylim(0, 1000) +
scale_fill_manual(values=gg_colour_hue(8)) +
theme(legend.position = 'none'))
p[lobe] = list(plot)
}
subplot(p[[1]], p[[2]], p[[3]], p[[4]], nrows=2)
rm(p, lobe, datExpr_lobe, plot)
datExpr = datExpr_backup # Just in case
# datMeta_backup = datMeta
vst_norm = function(lobe){
datExpr_lobe = datExpr %>% dplyr::select(which(datMeta$Brain_lobe==lobe))
datMeta_lobe = datMeta %>% filter(Brain_lobe==lobe & rownames(datMeta) %in% colnames(datExpr))
counts = as.matrix(datExpr_lobe)
rowRanges = GRanges(datProbes$chromosome_name,
IRanges(datProbes$start_position, width=datProbes$length),
strand=datProbes$strand,
feature_id=datProbes$ensembl_gene_id)
se = SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=datMeta_lobe)
dds = DESeqDataSet(se, design =~Diagnosis_)
#Estimate size factors
dds = estimateSizeFactors(dds)
vst_output = vst(dds)
datExpr_lobe = assay(vst_output)
# Filter out genes with 0 variance
to_keep = apply(datExpr_lobe, 1, sd)>0
print(glue(lobe,': Filtering out ', sum(!to_keep), ' probes with 0 variance, keeping ', sum(to_keep)))
datExpr_lobe = datExpr_lobe[to_keep,]
return(datExpr_lobe)
}
datExpr_postNorm = list()
for(lobe in names(table(datMeta$Brain_lobe))){
datExpr_postNorm[[lobe]] = vst_norm(lobe)
}
## Frontal: Filtering out 13 probes with 0 variance, keeping 33465
## Temporal: Filtering out 2 probes with 0 variance, keeping 33476
## Parietal: Filtering out 5 probes with 0 variance, keeping 33473
## Occipital: Filtering out 3 probes with 0 variance, keeping 33475
Regions: Frontal, Temporal, Parietal and Occipital
Similar behaviour in all regions
p = list()
for(lobe in names(table(datMeta$Brain_lobe))){
datExpr_lobe = datExpr_postNorm[[lobe]]
ASD_vs_CTL = make_ASD_vs_CTL_df(datExpr_lobe, lobe)
plot = ggplotly(ggplot(ASD_vs_CTL, aes(`gene-score`, abs(mean_diff), fill=`gene-score`)) +
geom_boxplot() + theme_minimal() +
scale_fill_manual(values=gg_colour_hue(8)) +
theme(legend.position = 'none'))
p[lobe] = list(plot)
}
subplot(p[[1]], p[[2]], p[[3]], p[[4]], nrows=2)
rm(p, datExpr_lobe, ASD_vs_CTL, plot)
Seems like the Parietal lobe is the only one with a significant number of DE genes
DE_by_region = function(datExpr, datMeta){
mod = model.matrix(~ Diagnosis_, data=datMeta)
corfit = duplicateCorrelation(datExpr, mod, block=datMeta$Subject_ID)
lmfit = lmFit(datExpr, design=mod, correlation=corfit$consensus)
fit = eBayes(lmfit, trend=T, robust=T)
top_genes = topTable(fit, coef=2, number=nrow(datExpr))
DE_info = top_genes[match(rownames(datExpr), rownames(top_genes)),]
}
DE_info_by_region = list()
i=1
for(lobe in names(table(datMeta$Brain_lobe))){
datExpr_lobe = datExpr_postNorm[[lobe]]
datMeta_lobe = datMeta %>% filter(Brain_lobe==lobe)
DE_info = DE_by_region(datExpr_lobe, datMeta_lobe) %>% mutate('ID'=rownames(datExpr_lobe))
DE_info_by_region[[i]] = DE_info
i = i+1
print(glue(lobe,' lobe: ', sum(DE_info$adj.P.Val<0.05 & DE_info$logFC>log2(1.2)),
' DE genes'))
}
## Frontal lobe: 0 DE genes
## Temporal lobe: 0 DE genes
## Parietal lobe: 252 DE genes
## Occipital lobe: 5 DE genes
names(DE_info_by_region) = names(table(datMeta$Brain_lobe))
rm(i, lobe, datExpr_lobe, datMeta_lobe, DE_info)
PC1 explains the average expression and PC2 log fold change
reduce_dim_datExpr = function(datExpr, datMeta, var_explained=0.95){
datExpr_pca = prcomp(datExpr, scale=TRUE)
last_pc = data.frame(summary(datExpr_pca)$importance[3,]) %>% rownames_to_column(var='ID') %>%
filter(.[[2]] >= var_explained) %>% top_n(-1, ID)
print(glue('Keeping top ', substr(last_pc$ID, 3, nchar(last_pc$ID)), ' components that explain ',
var_explained*100, '% of the variance'))
datExpr_top_pc = datExpr_pca$x %>% data.frame %>% dplyr::select(PC1:last_pc$ID)
return(list('datExpr'=datExpr_top_pc, 'pca_output'=datExpr_pca))
}
lobe = 'Parietal'
datExpr_lobe = datExpr %>% dplyr::select(which(datMeta$Brain_lobe==lobe))
datExpr_lobe = datExpr_lobe[apply(datExpr_lobe,1,sd)>0,]
datExpr_lobe = datExpr_lobe[DE_info_by_region[['Parietal']]$adj.P.Val<0.05,]
datMeta_lobe = datMeta %>% filter(Brain_lobe==lobe)
red_dim = reduce_dim_datExpr(datExpr_lobe, datMeta_lobe, 0.97)
## Keeping top 10 components that explain 97% of the variance
pca_lobe = red_dim$datExpr %>% mutate('ID' = rownames(red_dim$datExpr)) %>%
left_join(SFARI_genes, by='ID') %>% dplyr::select(ID, PC1, PC2, `gene-score`) %>%
mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
left_join(DE_info_by_region[[lobe]], by='ID') %>% mutate('abs_lFC'=abs(logFC)) %>%
mutate('gene-score'=ifelse(`gene-score`=='None' & ID %in% GO_neuronal$ID, 'Neuronal', `gene-score`))
selectable_scatter_plot(pca_lobe[,-1], pca_lobe[,-1])
# ggplotly(pca_lobe %>% ggplot(aes(PC1, PC2, fill=`gene-score`, color=`gene-score`)) +
# geom_point(alpha=0.5) + theme_minimal() + ggtitle(lobe) +
# scale_fill_manual(values=gg_colour_hue(7)) +
# scale_color_manual(values=gg_colour_hue(7)))
lfc=-1 means no filtering at all, the rest of the filterings include on top of the defined lfc, an adjusted p-value lower than 0.05
lfc_list = c(seq(0, 0.4, 0.2), seq(0.45, 1.5, 0.05))
n_genes = nrow(datExpr_lobe)
# Calculate PCAs
datExpr_pca_samps = datExpr_lobe %>% data.frame %>% t %>% prcomp(scale.=TRUE)
datExpr_pca_genes = datExpr_lobe %>% data.frame %>% prcomp(scale.=TRUE)
# Initialice DF to save PCA outputs
pcas_samps = datExpr_pca_samps$x %>% data.frame %>% dplyr::select(PC1:PC2) %>%
mutate('ID'=colnames(datExpr_lobe), 'lfc'=-1, PC1=scale(PC1), PC2=scale(PC2))
pcas_genes = datExpr_pca_genes$x %>% data.frame %>% dplyr::select(PC1:PC2) %>%
mutate('ID'=rownames(datExpr_lobe), 'lfc'=-1, PC1=scale(PC1), PC2=scale(PC2))
pca_samps_old = pcas_samps
pca_genes_old = pcas_genes
for(lfc in lfc_list){
# Filter DE genes with iteration's criteria
DE_genes = DE_info_by_region[[lobe]] %>% filter(adj.P.Val<0.05 & abs(logFC)>lfc)
datExpr_DE = datExpr_lobe %>% data.frame %>% filter(rownames(.) %in% DE_genes$ID)
n_genes = c(n_genes, nrow(DE_genes))
# Calculate PCAs
datExpr_pca_samps = datExpr_DE %>% t %>% prcomp(scale.=TRUE)
datExpr_pca_genes = datExpr_DE %>% prcomp(scale.=TRUE)
# Create new DF entries
pca_samps_new = datExpr_pca_samps$x %>% data.frame %>% dplyr::select(PC1:PC2) %>%
mutate('ID'=colnames(datExpr_lobe), 'lfc'=lfc, PC1=scale(PC1), PC2=scale(PC2))
pca_genes_new = datExpr_pca_genes$x %>% data.frame %>% dplyr::select(PC1:PC2) %>%
mutate('ID'=DE_genes$ID, 'lfc'=lfc, PC1=scale(PC1), PC2=scale(PC2))
# Change PC sign if necessary
if(cor(pca_samps_new$PC1, pca_samps_old$PC1)<0) pca_samps_new$PC1 = -pca_samps_new$PC1
if(cor(pca_samps_new$PC2, pca_samps_old$PC2)<0) pca_samps_new$PC2 = -pca_samps_new$PC2
if(cor(pca_genes_new$PC1, pca_genes_old[pca_genes_old$ID %in% pca_genes_new$ID,]$PC1 )<0){
pca_genes_new$PC1 = -pca_genes_new$PC1
}
if(cor(pca_genes_new$PC2, pca_genes_old[pca_genes_old$ID %in% pca_genes_new$ID,]$PC2 )<0){
pca_genes_new$PC2 = -pca_genes_new$PC2
}
pca_samps_old = pca_samps_new
pca_genes_old = pca_genes_new
# Update DFs
pcas_samps = rbind(pcas_samps, pca_samps_new)
pcas_genes = rbind(pcas_genes, pca_genes_new)
}
# Add Diagnosis/SFARI score information
pcas_samps = pcas_samps %>% mutate('ID'=substring(ID,2)) %>%
left_join(datMeta_lobe, by=c('ID'='Dissected_Sample_ID')) %>%
dplyr::select(ID, PC1, PC2, lfc, Diagnosis_, Brain_lobe)
pcas_genes = pcas_genes %>% left_join(SFARI_genes, by='ID') %>%
mutate('score'=as.factor(`gene-score`)) %>%
dplyr::select(ID, PC1, PC2, lfc, score)
# Plot change of number of genes
ggplotly(data.frame('lfc'=lfc_list, 'n_genes'=n_genes[-1]) %>% ggplot(aes(x=lfc, y=n_genes)) +
geom_point() + geom_line() + theme_minimal() +
ggtitle('Number of remaining genes when modifying filtering threshold'))
rm(datExpr_pca_genes, datExpr_pca_samps, DE_genes, datExpr_DE, pca_genes_new, pca_samps_new,
pca_genes_old, pca_samps_old, lfc_list, lfc)
Note: PC values get smaller as Log2 fold change increases, so on each iteration the values were scaled so it would be easier to compare between frames
ggplotly(pcas_samps %>% ggplot(aes(PC1, PC2, color=Diagnosis_)) + geom_point(aes(frame=lfc, ids=ID)) +
theme_minimal() + ggtitle('Samples PCA plot modifying filtering threshold'))
pcas_sfari_genes = pcas_genes %>% filter(!is.na(score)) %>% dplyr::select(-'score')
complete_sfari_df = expand.grid(unique(pcas_sfari_genes$ID), unique(pcas_sfari_genes$lfc))
colnames(complete_sfari_df) = c('ID', 'lfc')
pcas_sfari_genes = pcas_sfari_genes %>% right_join(complete_sfari_df, by=c('ID','lfc')) %>%
left_join(SFARI_genes, by='ID') %>%
mutate('score'=as.factor(`gene-score`), 'syndromic'=as.factor(syndromic))
pcas_sfari_genes[is.na(pcas_sfari_genes)] = 0 # Fix for ghost points
ggplotly(pcas_sfari_genes %>% ggplot(aes(PC1, PC2, color=score)) +
geom_point(aes(frame=lfc, ids=ID), alpha=0.6) + theme_minimal() +
ggtitle('Genes PCA plot modifying filtering threshold'))
table(SFARI_genes$`gene-score`[SFARI_genes$ID %in% DE_info_by_region[[lobe]]$ID[DE_info_by_region[[lobe]]$adj.P.Val<0.05]])
##
## 3 4 5
## 4 9 7
# Calculate percentage of genes remaining on each lfc by each score
score_count_by_lfc = pcas_genes %>% filter(!is.na(score)) %>% group_by(lfc, score) %>% tally %>% ungroup
score_count_pcnt = score_count_by_lfc %>% filter(lfc==-1) %>% mutate('n_init'=n) %>%
dplyr::select(score, n_init) %>% right_join(score_count_by_lfc, by='score') %>%
mutate('pcnt'=round(n/n_init*100, 2)) %>% filter(lfc!=-1)
# Complete missing entries with zeros
complete_score_count_pcnt = expand.grid(unique(score_count_pcnt$lfc), unique(score_count_pcnt$score))
colnames(complete_score_count_pcnt) = c('lfc', 'score')
score_count_pcnt = full_join(score_count_pcnt, complete_score_count_pcnt, by=c('lfc','score')) %>%
dplyr::select(score, lfc, n, pcnt)
score_count_pcnt[is.na(score_count_pcnt)] = 0
# Join counts by score and all genes
all_count_pcnt = pcas_genes %>% group_by(lfc) %>% tally %>% filter(lfc!=-1) %>%
mutate('pcnt'=round(n/nrow(datExpr)*100, 2), 'score'='All')
score_count_pcnt = rbind(score_count_pcnt, all_count_pcnt)
ggplotly(score_count_pcnt %>% ggplot(aes(lfc, pcnt, color=score)) + geom_point() + geom_line() +
theme_minimal() +
ggtitle('% of points left after each increase in log2 fold change'))
rm(score_count_by_lfc, complete_score_count_pcnt)
ggplotly(pcas_genes %>% ggplot(aes(PC1, PC2)) + geom_point(aes(frame=lfc, ids=ID, alpha=0.3)) +
theme_minimal() + ggtitle('Genes PCA plot modifying filtering threshold'))
Using adjusted p-value < 0.05 and logFC>log2(1.2)
DE_genes = DE_info_by_region[[lobe]] %>% filter(adj.P.Val<0.05 & abs(logFC)>log2(1.2))
datExpr_DE = datExpr_lobe %>% data.frame %>% filter(rownames(.) %in% DE_genes$ID)
datExpr_pca_genes = datExpr_DE %>% data.frame %>% prcomp(scale.=TRUE)
pca_genes = datExpr_pca_genes$x %>% data.frame %>% dplyr::select(PC1:PC2) %>%
mutate('ID'=DE_genes$ID, PC1=PC1, PC2=PC2)
pca_genes = pca_genes %>% left_join(SFARI_genes, by='ID') %>%
mutate('score'=as.factor(`gene-score`)) %>%
dplyr::select(ID, PC1, PC2, score)
manual_clusters = as.factor(as.numeric(-0.145*pca_genes$PC1 - 0.3 > pca_genes$PC2))
ggplotly(pca_genes %>% ggplot(aes(PC1, PC2, color=manual_clusters)) + geom_point(aes(id=ID)) +
geom_abline(slope=-0.145, intercept=-0.3, color='gray') + theme_minimal())
rm(DE_genes, datExpr_pca_genes)
manual_clusters_data = cbind(apply(datExpr_DE, 1, mean), apply(datExpr_DE, 1, sd),
manual_clusters) %>% data.frame
colnames(manual_clusters_data) = c('mean','sd','cluster')
manual_clusters_data = manual_clusters_data %>% mutate('cluster'=as.factor(cluster))
p1 = ggplotly(manual_clusters_data %>% ggplot(aes(x=mean, color=cluster, fill=cluster)) +
geom_density(alpha=0.4) + theme_minimal())
p2 = ggplotly(manual_clusters_data %>% ggplot(aes(x=sd, color=cluster, fill=cluster)) +
geom_density(alpha=0.4) + theme_minimal())
subplot(p1, p2, nrows=1)